Advanced change detection techniques primarily target image pairs of equal
and high quality. However, variations in imaging conditions and platforms
frequently lead to image pairs with distinct qualities: one image being
high-quality, while the other being low-quality. These disparities in image
quality present significant challenges for understanding image pairs
semantically and extracting change features, ultimately resulting in a notable
decline in performance. To tackle this challenge, we introduce an innovative
training strategy grounded in knowledge distillation. The core idea revolves
around leveraging task knowledge acquired from high-quality image pairs to
guide the model's learning process when dealing with image pairs that exhibit
differences in quality. Additionally, we develop a hierarchical correlation
distillation approach (involving self-correlation, cross-correlation, and
global correlation). This approach compels the student model to replicate the
correlations inherent in the teacher model, rather than focusing solely on
individual features. This ensures effective knowledge transfer while
maintaining the student model's training flexibility.

应对图像品质差异带来的挑战，我们引入了一种基于知识蒸馏的创新训练策略，以高质量图像对获取的任务知识指导处理品质不同的图像对的学习过程。此外，我们开发了一种层次相关性蒸馏方法，通过复制教师模型中固有的相关性，而不仅仅关注个体特征，来确保有效地传递知识并保持学生模型的训练灵活性。

HiCD: 基于分层相关蒸馏的质量变化图像变化检测

HiCD: Change Detection in Quality-Varied Images via Hierarchical  Correlation Distillation

Few-shot semantic segmentation (FSS) aims to form class-agnostic models
segmenting unseen classes with only a handful of annotations. Previous methods
limited to the semantic feature and prototype representation suffer from coarse
segmentation granularity and train-set overfitting. In this work, we design
Hierarchically Decoupled Matching Network (HDMNet) mining pixel-level support
correlation based on the transformer architecture. The self-attention modules
are used to assist in establishing hierarchical dense features, as a means to
accomplish the cascade matching between query and support features. Moreover,
we propose a matching module to reduce train-set overfitting and introduce
correlation distillation leveraging semantic correspondence from coarse
resolution to boost fine-grained segmentation. Our method performs decently in
experiments. We achieve 50.0% mIoU on COCO dataset one-shot setting and 56.0%
on five-shot segmentation, respectively. The code will be available on the
project website. We hope our work can benefit broader industrial applications
where novel classes with limited annotations are required to be decently
identified.

本文提出了基于 Hierarchically Decoupled Matching Network (HDMNet) 的 Few-shot 语义分割方法，使用 transformer 架构、自注意力模块和匹配模块来实现级联匹配和关键像素级别的支持关联，且通过语义对应度实现了从粗糙分辨率到细粒度分割的相关性蒸馏，实验结果表明在 COCO 数据集中的 few-shot 语义分割任务上取得了较好的性能。

层次化稠密相关蒸馏用于少样本分割 - 报告摘要

Hierarchical Dense Correlation Distillation for Few-Shot  Segmentation-Extended Abstract

Few-shot semantic segmentation (FSS) aims to form class-agnostic models
segmenting unseen classes with only a handful of annotations. Previous methods
limited to the semantic feature and prototype representation suffer from coarse
segmentation granularity and train-set overfitting. In this work, we design
Hierarchically Decoupled Matching Network (HDMNet) mining pixel-level support
correlation based on the transformer architecture. The self-attention modules
are used to assist in establishing hierarchical dense features, as a means to
accomplish the cascade matching between query and support features. Moreover,
we propose a matching module to reduce train-set overfitting and introduce
correlation distillation leveraging semantic correspondence from coarse
resolution to boost fine-grained segmentation. Our method performs decently in
experiments. We achieve $50.0\%$ mIoU on \coco~dataset one-shot setting and
$56.0\%$ on five-shot segmentation, respectively.

本文提出了一种基于 transformer 架构的 Hierarchically Decoupled Matching Network（HDMNet）用于 few-shot 语义分割任务。该网络利用自注意力机制来建立层次化的密集特征并使用匹配模块降低过拟合，引入来自粗分辨率的语义对应的相关性传递，以提高细粒度的分割。在实验中该方法表现良好，在 COCO 数据集上的 1-shot 和 5-shot 分割分别达到了 50.0% 和 56.0% 的 mIoU。